# ------------------------------------------------------------------------------------
# NeRF-Factory
# Copyright (c) 2022 POSTECH, KAIST, Kakao Brain Corp. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------
# Modified from DVGO (https://github.com/sunset1995/DirectVoxGO)
# Copyright (c) 2022 Google LLC. All Rights Reserved.
# ------------------------------------------------------------------------------------

import time

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_scatter import segment_coo

import src.model.dvgo.grid as grid
from src.model.dvgo.__global__ import *
from src.model.dvgo.dmpigo import create_full_step_id
from src.model.dvgo.dvgo import Alphas2Weights, Raw2Alpha

"""Model"""


class DirectContractedVoxGO(nn.Module):
    def __init__(
        self,
        xyz_min,
        xyz_max,
        num_voxels=0,
        num_voxels_base=0,
        alpha_init=None,
        mask_cache_world_size=None,
        fast_color_thres=0,
        bg_len=0.2,
        contracted_norm="inf",
        density_type="DenseGrid",
        k0_type="DenseGrid",
        density_config={},
        k0_config={},
        rgbnet_dim=0,
        rgbnet_depth=3,
        rgbnet_width=128,
        viewbase_pe=4,
        **kwargs,
    ):
        super(DirectContractedVoxGO, self).__init__()
        # xyz_min/max are the boundary that separates fg and bg scene

        xyz_min = torch.Tensor(xyz_min)
        xyz_max = torch.Tensor(xyz_max)
        assert len(
            ((xyz_max - xyz_min) * 100000).long().unique()
        ), "scene bbox must be a cube in DirectContractedVoxGO"
        self.register_buffer("scene_center", (xyz_min + xyz_max) * 0.5)
        self.register_buffer("scene_radius", (xyz_max - xyz_min) * 0.5)
        self.register_buffer("xyz_min", torch.Tensor([-1, -1, -1]) - bg_len)
        self.register_buffer("xyz_max", torch.Tensor([1, 1, 1]) + bg_len)
        if isinstance(fast_color_thres, dict):
            self._fast_color_thres = fast_color_thres
            self.fast_color_thres = fast_color_thres[0]
        else:
            self._fast_color_thres = None
            self.fast_color_thres = fast_color_thres
        self.bg_len = bg_len
        self.contracted_norm = contracted_norm

        # determine based grid resolution
        self.num_voxels_base = num_voxels_base
        self.voxel_size_base = (
            (self.xyz_max - self.xyz_min).prod() / self.num_voxels_base
        ).pow(1 / 3)

        # determine init grid resolution
        self._set_grid_resolution(num_voxels)

        # determine the density bias shift
        self.alpha_init = alpha_init
        self.register_buffer(
            "act_shift", torch.FloatTensor([np.log(1 / (1 - alpha_init) - 1)])
        )
        print("dcvgo: set density bias shift to", self.act_shift)

        # init density voxel grid
        self.density_type = density_type
        self.density_config = density_config
        self.density = grid.create_grid(
            density_type,
            channels=1,
            world_size=self.world_size,
            xyz_min=self.xyz_min,
            xyz_max=self.xyz_max,
            config=self.density_config,
        )

        # init color representation
        self.rgbnet_kwargs = {
            "rgbnet_dim": rgbnet_dim,
            "rgbnet_depth": rgbnet_depth,
            "rgbnet_width": rgbnet_width,
            "viewbase_pe": viewbase_pe,
        }
        self.k0_type = k0_type
        self.k0_config = k0_config
        if rgbnet_dim <= 0:
            # color voxel grid  (coarse stage)
            self.k0_dim = 3
            self.k0 = grid.create_grid(
                k0_type,
                channels=self.k0_dim,
                world_size=self.world_size,
                xyz_min=self.xyz_min,
                xyz_max=self.xyz_max,
                config=self.k0_config,
            )
            self.rgbnet = None
        else:
            # feature voxel grid + shallow MLP  (fine stage)
            self.k0_dim = rgbnet_dim
            self.k0 = grid.create_grid(
                k0_type,
                channels=self.k0_dim,
                world_size=self.world_size,
                xyz_min=self.xyz_min,
                xyz_max=self.xyz_max,
                config=self.k0_config,
            )
            self.register_buffer(
                "viewfreq", torch.FloatTensor([(2**i) for i in range(viewbase_pe)])
            )
            dim0 = 3 + 3 * viewbase_pe * 2
            dim0 += self.k0_dim
            self.rgbnet = nn.Sequential(
                nn.Linear(dim0, rgbnet_width),
                nn.ReLU(inplace=True),
                *[
                    nn.Sequential(
                        nn.Linear(rgbnet_width, rgbnet_width), nn.ReLU(inplace=True)
                    )
                    for _ in range(rgbnet_depth - 2)
                ],
                nn.Linear(rgbnet_width, 3),
            )
            nn.init.constant_(self.rgbnet[-1].bias, 0)
            print("dcvgo: feature voxel grid", self.k0)
            print("dcvgo: mlp", self.rgbnet)

        # Using the coarse geometry if provided (used to determine known free space and unknown space)
        # Re-implement as occupancy grid (2021/1/31)
        if mask_cache_world_size is None:
            mask_cache_world_size = self.world_size
        mask = torch.ones(list(mask_cache_world_size), dtype=torch.bool)
        self.mask_cache = grid.MaskGrid(
            path=None, mask=mask, xyz_min=self.xyz_min, xyz_max=self.xyz_max
        )

    def _set_grid_resolution(self, num_voxels):
        # Determine grid resolution
        self.num_voxels = num_voxels
        self.voxel_size = ((self.xyz_max - self.xyz_min).prod() / num_voxels).pow(1 / 3)
        self.world_size = ((self.xyz_max - self.xyz_min) / self.voxel_size).long()
        self.world_len = self.world_size[0].item()
        self.voxel_size_ratio = self.voxel_size / self.voxel_size_base
        print("dcvgo: voxel_size      ", self.voxel_size)
        print("dcvgo: world_size      ", self.world_size)
        print("dcvgo: voxel_size_base ", self.voxel_size_base)
        print("dcvgo: voxel_size_ratio", self.voxel_size_ratio)

    def get_kwargs(self):
        return {
            "xyz_min": self.xyz_min.cpu().numpy(),
            "xyz_max": self.xyz_max.cpu().numpy(),
            "num_voxels": self.num_voxels,
            "num_voxels_base": self.num_voxels_base,
            "alpha_init": self.alpha_init,
            "voxel_size_ratio": self.voxel_size_ratio,
            "mask_cache_world_size": list(self.mask_cache.mask.shape),
            "fast_color_thres": self.fast_color_thres,
            "contracted_norm": self.contracted_norm,
            "density_type": self.density_type,
            "k0_type": self.k0_type,
            "density_config": self.density_config,
            "k0_config": self.k0_config,
            **self.rgbnet_kwargs,
        }

    @torch.no_grad()
    def scale_volume_grid(self, num_voxels):
        print("dcvgo: scale_volume_grid start")
        ori_world_size = self.world_size
        self._set_grid_resolution(num_voxels)
        print(
            "dcvgo: scale_volume_grid scale world_size from",
            ori_world_size.tolist(),
            "to",
            self.world_size.tolist(),
        )

        self.density.scale_volume_grid(self.world_size)
        self.k0.scale_volume_grid(self.world_size)

        if np.prod(self.world_size.tolist()) <= 256**3:
            self_grid_xyz = torch.stack(
                torch.meshgrid(
                    torch.linspace(
                        self.xyz_min[0], self.xyz_max[0], self.world_size[0]
                    ),
                    torch.linspace(
                        self.xyz_min[1], self.xyz_max[1], self.world_size[1]
                    ),
                    torch.linspace(
                        self.xyz_min[2], self.xyz_max[2], self.world_size[2]
                    ),
                ),
                -1,
            )
            self_alpha = F.max_pool3d(
                self.activate_density(self.density.get_dense_grid()),
                kernel_size=3,
                padding=1,
                stride=1,
            )[0, 0]
            self.mask_cache = grid.MaskGrid(
                path=None,
                mask=self.mask_cache(self_grid_xyz)
                & (self_alpha > self.fast_color_thres),
                xyz_min=self.xyz_min,
                xyz_max=self.xyz_max,
            )

        print("dcvgo: scale_volume_grid finish")

    @torch.no_grad()
    def update_occupancy_cache(self):
        ori_p = self.mask_cache.mask.float().mean().item()
        cache_grid_xyz = torch.stack(
            torch.meshgrid(
                torch.linspace(
                    self.xyz_min[0], self.xyz_max[0], self.mask_cache.mask.shape[0]
                ),
                torch.linspace(
                    self.xyz_min[1], self.xyz_max[1], self.mask_cache.mask.shape[1]
                ),
                torch.linspace(
                    self.xyz_min[2], self.xyz_max[2], self.mask_cache.mask.shape[2]
                ),
            ),
            -1,
        )
        cache_grid_density = self.density(cache_grid_xyz)[None, None]
        cache_grid_alpha = self.activate_density(cache_grid_density)
        cache_grid_alpha = F.max_pool3d(
            cache_grid_alpha, kernel_size=3, padding=1, stride=1
        )[0, 0]
        self.mask_cache.mask &= cache_grid_alpha > self.fast_color_thres
        new_p = self.mask_cache.mask.float().mean().item()
        print(f"dcvgo: update mask_cache {ori_p:.4f} => {new_p:.4f}")

    def update_occupancy_cache_lt_nviews(
        self, rays_o_tr, rays_d_tr, imsz, render_kwargs, maskout_lt_nviews
    ):
        print("dcvgo: update mask_cache lt_nviews start")
        eps_time = time.time()
        count = torch.zeros_like(self.density.get_dense_grid()).long()
        device = count.device
        for rays_o_, rays_d_ in zip(rays_o_tr.split(imsz), rays_d_tr.split(imsz)):
            ones = grid.DenseGrid(1, self.world_size, self.xyz_min, self.xyz_max)
            for rays_o, rays_d in zip(rays_o_.split(8192), rays_d_.split(8192)):
                ray_pts, inner_mask, t = self.sample_ray(
                    ori_rays_o=rays_o.to(device),
                    ori_rays_d=rays_d.to(device),
                    **render_kwargs,
                )
                ones(ray_pts).sum().backward()
            count.data += ones.grid.grad > 1
        ori_p = self.mask_cache.mask.float().mean().item()
        self.mask_cache.mask &= (count >= maskout_lt_nviews)[0, 0]
        new_p = self.mask_cache.mask.float().mean().item()
        print(f"dcvgo: update mask_cache {ori_p:.4f} => {new_p:.4f}")
        eps_time = time.time() - eps_time
        print(f"dcvgo: update mask_cache lt_nviews finish (eps time:", eps_time, "sec)")

    def density_total_variation_add_grad(self, weight, dense_mode):
        w = weight * self.world_size.max() / 128
        self.density.total_variation_add_grad(w, w, w, dense_mode)

    def k0_total_variation_add_grad(self, weight, dense_mode):
        w = weight * self.world_size.max() / 128
        self.k0.total_variation_add_grad(w, w, w, dense_mode)

    def activate_density(self, density, interval=None):
        interval = interval if interval is not None else self.voxel_size_ratio
        shape = density.shape
        return Raw2Alpha.apply(density.flatten(), self.act_shift, interval).reshape(
            shape
        )

    def sample_ray(
        self, ori_rays_o, ori_rays_d, stepsize, is_train=False, **render_kwargs
    ):
        """Sample query points on rays.
        All the output points are sorted from near to far.
        Input:
            rays_o, rayd_d:   both in [N, 3] indicating ray configurations.
            stepsize:         the number of voxels of each sample step.
        Output:
            ray_pts:          [M, 3] storing all the sampled points.
            ray_id:           [M]    the index of the ray of each point.
            step_id:          [M]    the i'th step on a ray of each point.
        """
        rays_o = (ori_rays_o - self.scene_center) / self.scene_radius
        rays_d = ori_rays_d / ori_rays_d.norm(dim=-1, keepdim=True)
        N_inner = int(2 / (2 + 2 * self.bg_len) * self.world_len / stepsize) + 1
        N_outer = N_inner
        b_inner = torch.linspace(0, 2, N_inner + 1)
        b_outer = 2 / torch.linspace(1, 1 / 128, N_outer + 1)
        t = torch.cat(
            [
                (b_inner[1:] + b_inner[:-1]) * 0.5,
                (b_outer[1:] + b_outer[:-1]) * 0.5,
            ]
        ).type_as(rays_o)
        ray_pts = rays_o[:, None, :] + rays_d[:, None, :] * t[None, :, None]
        if self.contracted_norm == "inf":
            norm = ray_pts.abs().amax(dim=-1, keepdim=True)
        elif self.contracted_norm == "l2":
            norm = ray_pts.norm(dim=-1, keepdim=True)
        else:
            raise NotImplementedError
        inner_mask = norm <= 1
        ray_pts = torch.where(
            inner_mask,
            ray_pts,
            ray_pts / norm * ((1 + self.bg_len) - self.bg_len / norm),
        )
        return ray_pts, inner_mask.squeeze(-1), t

    def forward(
        self,
        rays_o,
        rays_d,
        viewdirs,
        global_step=None,
        is_train=False,
        **render_kwargs,
    ):
        """Volume rendering
        @rays_o:   [N, 3] the starting point of the N shooting rays.
        @rays_d:   [N, 3] the shooting direction of the N rays.
        @viewdirs: [N, 3] viewing direction to compute positional embedding for MLP.
        """
        assert (
            len(rays_o.shape) == 2 and rays_o.shape[-1] == 3
        ), "Only suuport point queries in [N, 3] format"
        if (
            isinstance(self._fast_color_thres, dict)
            and global_step in self._fast_color_thres.keys()
        ):
            print(
                f"dcvgo: update fast_color_thres {self.fast_color_thres} => {self._fast_color_thres[global_step]}"
            )
            self.fast_color_thres = self._fast_color_thres[global_step]

        ret_dict = {}
        N = len(rays_o)

        # sample points on rays
        ray_pts, inner_mask, t = self.sample_ray(
            ori_rays_o=rays_o,
            ori_rays_d=rays_d,
            is_train=global_step is not None,
            **render_kwargs,
        )
        n_max = len(t)
        interval = render_kwargs["stepsize"] * self.voxel_size_ratio
        ray_id, step_id = create_full_step_id(ray_pts.shape[:2])

        # skip oversampled points outside scene bbox
        mask = inner_mask.clone()
        dist_thres = (
            (2 + 2 * self.bg_len) / self.world_len * render_kwargs["stepsize"] * 0.95
        )
        dist = (ray_pts[:, 1:] - ray_pts[:, :-1]).norm(dim=-1)
        from src.model.dvgo.__global__ import ub360_utils_cuda

        mask[:, 1:] |= ub360_utils_cuda.cumdist_thres(dist, dist_thres)
        ray_pts = ray_pts[mask]
        inner_mask = inner_mask[mask]
        t = t[None].repeat(N, 1)[mask]
        ray_id = ray_id[mask.flatten()]
        step_id = step_id[mask.flatten()]

        # skip known free space
        mask = self.mask_cache(ray_pts)
        ray_pts = ray_pts[mask]
        inner_mask = inner_mask[mask]
        t = t[mask]
        ray_id = ray_id[mask]
        step_id = step_id[mask]

        # query for alpha w/ post-activation
        density = self.density(ray_pts)
        alpha = self.activate_density(density, interval)
        if self.fast_color_thres > 0:
            mask = alpha > self.fast_color_thres
            ray_pts = ray_pts[mask]
            inner_mask = inner_mask[mask]
            t = t[mask]
            ray_id = ray_id[mask]
            step_id = step_id[mask]
            density = density[mask]
            alpha = alpha[mask]

        # compute accumulated transmittance
        weights, alphainv_last = Alphas2Weights.apply(alpha, ray_id, N)
        if self.fast_color_thres > 0:
            mask = weights > self.fast_color_thres
            ray_pts = ray_pts[mask]
            inner_mask = inner_mask[mask]
            t = t[mask]
            ray_id = ray_id[mask]
            step_id = step_id[mask]
            density = density[mask]
            alpha = alpha[mask]
            weights = weights[mask]

        # query for color
        k0 = self.k0(ray_pts)
        if self.rgbnet is None:
            # no view-depend effect
            rgb = torch.sigmoid(k0)
        else:
            # view-dependent color emission
            viewdirs_emb = (viewdirs.unsqueeze(-1) * self.viewfreq).flatten(-2)
            viewdirs_emb = torch.cat(
                [viewdirs, viewdirs_emb.sin(), viewdirs_emb.cos()], -1
            )
            viewdirs_emb = viewdirs_emb.flatten(0, -2)[ray_id]
            rgb_feat = torch.cat([k0, viewdirs_emb], -1)
            rgb_logit = self.rgbnet(rgb_feat)
            rgb = torch.sigmoid(rgb_logit)

        # Ray marching
        rgb_marched = segment_coo(
            src=(weights.unsqueeze(-1) * rgb),
            index=ray_id.to(weights.device),
            out=torch.zeros([N, 3], device=weights.device),
            reduce="sum",
        )
        if render_kwargs.get("rand_bkgd", False) and is_train:
            rgb_marched += alphainv_last.unsqueeze(-1) * torch.rand_like(rgb_marched)
        else:
            rgb_marched += alphainv_last.unsqueeze(-1) * render_kwargs["bg"]
        wsum_mid = segment_coo(
            src=weights[inner_mask],
            index=ray_id.to(weights.device)[inner_mask],
            out=torch.zeros([N], device=weights.device),
            reduce="sum",
        )
        s = 1 - 1 / (1 + t)  # [0, inf] => [0, 1]
        ret_dict.update(
            {
                "alphainv_last": alphainv_last,
                "weights": weights,
                "wsum_mid": wsum_mid,
                "rgb_marched": rgb_marched,
                "raw_density": density,
                "raw_alpha": alpha,
                "raw_rgb": rgb,
                "ray_id": ray_id,
                "step_id": step_id,
                "n_max": n_max,
                "t": t,
                "s": s,
            }
        )

        if render_kwargs.get("render_depth", False):
            with torch.no_grad():
                depth = segment_coo(
                    src=(weights * s),
                    index=ray_id.to(device=weights.device),
                    out=torch.zeros([N], device=weights.device),
                    reduce="sum",
                )
            ret_dict.update({"depth": depth})

        return ret_dict


class DistortionLoss(torch.autograd.Function):
    @staticmethod
    def forward(ctx, w, s, n_max, ray_id):
        n_rays = ray_id.max() + 1
        interval = 1 / n_max
        from src.model.dvgo.__global__ import ub360_utils_cuda

        w_prefix, w_total, ws_prefix, ws_total = ub360_utils_cuda.segment_cumsum(
            w, s, ray_id
        )
        loss_uni = (1 / 3) * interval * w.pow(2)
        loss_bi = 2 * w * (s * w_prefix - ws_prefix)
        ctx.save_for_backward(w, s, w_prefix, w_total, ws_prefix, ws_total, ray_id)
        ctx.interval = interval
        return (loss_bi.sum() + loss_uni.sum()) / n_rays

    @staticmethod
    @torch.autograd.function.once_differentiable
    def backward(ctx, grad_back):
        w, s, w_prefix, w_total, ws_prefix, ws_total, ray_id = ctx.saved_tensors
        interval = ctx.interval
        grad_uni = (1 / 3) * interval * 2 * w
        w_suffix = w_total[ray_id] - (w_prefix + w)
        ws_suffix = ws_total[ray_id] - (ws_prefix + w * s)
        grad_bi = 2 * (s * (w_prefix - w_suffix) + (ws_suffix - ws_prefix))
        grad = grad_back * (grad_bi + grad_uni)
        return grad, None, None, None
